8 September 2026 · DMARC Engine · 36 min read
Introduction to Seasonal Sending Patterns and DMARC
Domains with seasonal sending patterns present a unique challenge for DMARC policy management, as the fluctuating email volumes can significantly impact the effectiveness of their DMARC setup. For instance, an e-commerce company like Amazon or a travel booking website such as Expedia will typically experience a surge in email sending during holiday seasons or special promotions, which can lead to a higher risk of email authentication failures and potential delivery issues.
In our experience at DMARC Engine, we have seen many customers struggle to optimise their DMARC policy for seasonal sending patterns, often resulting in reduced deliverability or unnecessary quarantine of legitimate emails.
A common issue we encounter is the misconfiguration of DMARC policies, which can be exacerbated by seasonal sending patterns. For example, a domain may have a DMARC policy set to p=quarantine with a percentage of 100, which can lead to a high volume of emails being quarantined during peak sending periods.
To mitigate this, we recommend setting up a DMARC policy with a more nuanced approach, such as p=none with a percentage of 20, and gradually increasing the percentage over time. This allows the domain to monitor and adjust to the changing email landscape without compromising deliverability.
In a hosted or managed setup, such as the one provided by DMARC Engine, this process can be automated and optimised based on the customer's specific needs and sending patterns.
For instance, our system can automatically adjust the DMARC policy based on the customer's email volume and authentication results, ensuring that the policy remains effective and aligned with their sending patterns.
To illustrate this, let's consider an example of a DMARC record for a domain with seasonal sending patterns:
_dmarc.example.com. IN TXT "v=DMARC1; p=none; pct=20; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1"In this example, the DMARC record is set to p=none with a percentage of 20, which means that 20% of emails that fail DMARC authentication will be quarantined. The rua and ruf tags specify the email addresses that will receive aggregate and failure reports, respectively.
The fo tag is set to 1, which means that the domain will receive failure reports for emails that fail DMARC authentication.
By analysing these reports, the domain owner can gain valuable insights into their email authentication and make data-driven decisions to optimise their DMARC policy.
In our experience, domains with seasonal sending patterns often require more frequent monitoring and adjustments to their DMARC policy, particularly during peak sending periods.
By working closely with our customers and leveraging our expertise in DMARC policy management, we can help them navigate these challenges and ensure that their email deliverability remains optimal throughout the year.
One of the key considerations for domains with seasonal sending patterns is the potential impact on their reputation and deliverability.
If a domain's DMARC policy is not optimised for seasonal sending, it can lead to a higher risk of email authentication failures, which can negatively impact their reputation and deliverability.
To mitigate this, we recommend that domains with seasonal sending patterns implement a robust monitoring and reporting system, such as the one provided by DMARC Engine, to track their email authentication and make data-driven decisions to optimise their DMARC policy.
By taking a proactive and data-driven approach to DMARC policy management, domains with seasonal sending patterns can ensure that their email deliverability remains optimal and their reputation is protected.
In the next section, we will delve deeper into the impact of seasonal volume on DMARC policy and explore strategies for assessing and adjusting DMARC policies to accommodate seasonal sending patterns.
For now, it is essential to recognise that domains with seasonal sending patterns require a more nuanced and dynamic approach to DMARC policy management, one that takes into account the fluctuating email volumes and authentication requirements.
By understanding these complexities and leveraging the right tools and expertise, domains can optimise their DMARC policy and ensure that their email deliverability remains optimal throughout the year.
To achieve this, it is crucial to have a deep understanding of the domain's email sending patterns, authentication requirements, and DMARC policy configuration.
At DMARC Engine, we work closely with our customers to gain a comprehensive understanding of their email ecosystem and provide tailored guidance and support to optimise their DMARC policy for seasonal sending patterns.
By combining our expertise with the customer's knowledge of their email sending patterns, we can develop a DMARC policy that is tailored to their specific needs and optimised for seasonal sending.
This collaborative approach enables us to provide the most effective and efficient DMARC policy management solutions for domains with seasonal sending patterns.
In the following sections, we will explore the intricacies of DMARC policy management for domains with seasonal sending patterns, including the impact of seasonal volume on DMARC policy, assessing current DMARC policy for seasonal adjustments, and step-by-step guides for adjusting DMARC policy for seasonal sending.
We will also examine common pitfalls in DMARC policy management for seasonal senders and provide recommendations for optimising DMARC policy for multiple domains and subdomains.
By the end of this article, readers will have a comprehensive understanding of the complexities and challenges associated with DMARC policy management for domains with seasonal sending patterns, as well as practical guidance and recommendations for optimising their DMARC policy and ensuring optimal email deliverability throughout the year.
To illustrate the complexity of DMARC policy management for seasonal senders, consider the example of a retail company that sends a high volume of promotional emails during the holiday season.
Their DMARC record might look like this:
_dmarc.example.com. IN TXT "v=DMARC1; p=quarantine; pct=50; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1"In this example, the DMARC record is set to p=quarantine with a percentage of 50, which
Understanding the Impact of Seasonal Volume on DMARC Policy
When managing DMARC policy for domains with seasonal sending patterns, it is crucial to consider the impact of volume fluctuations on the policy's effectiveness. A domain that sends a high volume of emails during certain periods, such as holidays or special events, may experience a significant increase in failed DMARC authentications. This can be attributed to various factors, including the use of third-party senders, affiliate marketing, or promotional campaigns that may not be fully aligned with the domain's DMARC policy.
For instance, a retail company may engage in a large-scale promotional campaign during the holiday season, resulting in a substantial increase in email volume. If the company's DMARC policy is set to a strict quarantine or reject policy, the sudden surge in email volume may lead to a higher number of false positives, where legitimate emails are incorrectly flagged as spam or rejected by recipient mail servers. This can have a negative impact on the company's email deliverability and reputation.
To mitigate this issue, it is essential to monitor and analyse the domain's DMARC aggregate reports, which provide valuable insights into the email authentication and delivery process. By examining the reports, domain owners can identify potential issues and adjust their DMARC policy accordingly. For example, the following aggregate report snippet highlights a significant increase in email volume during the holiday season:
<record>
<row>
<source_ip>192.0.2.1</source_ip>
<count>1000</count>
<policy_evaluated>
<disposition>quarantine</disposition>
<dkim>fail</dkim>
<spf>fail</spf>
</policy_evaluated>
</row>
<identifiers>
<header_from>example.com</header_from>
</identifiers>
<date_range>
<begin>2022-12-01</begin>
<end>2022-12-31</end>
</date_range>
</record>In this example, the report indicates that the domain example.com experienced a high volume of emails with failed DKIM and SPF authentications during the month of December. This could be due to the use of third-party senders or affiliate marketing campaigns that are not properly configured to align with the domain's DMARC policy.
To address this issue, domain owners may need to adjust their DMARC policy to a more relaxed setting, such as a none or quarantine policy, to allow for a higher volume of emails to be delivered during peak sending periods. However, this approach requires careful consideration, as it may also increase the risk of spam and phishing attacks. A more effective approach may be to implement a tiered DMARC policy, where the policy is relaxed during peak sending periods and then tightened during off-peak periods.
In a hosted or managed setup, such as the one provided by DMARC Engine, domain owners can leverage advanced analytics and reporting tools to monitor and adjust their DMARC policy in real-time. This can help to optimise email deliverability and reduce the risk of false positives or spam and phishing attacks. Also, a managed setup can provide access to expert guidance and support, which can be invaluable in navigating the complexities of DMARC policy management.
Another important consideration when managing DMARC policy for seasonal senders is the impact of volume fluctuations on the domain's reputation. A sudden increase in email volume can lead to a surge in complaints and spam reports, which can negatively impact the domain's reputation and deliverability. To mitigate this risk, domain owners can implement a range of strategies, including email list segmentation, content optimisation, and complaint feedback loops.
In terms of specific recommendations, domain owners with seasonal sending patterns should consider the following best practices:
- Monitor DMARC aggregate reports closely during peak sending periods to identify potential issues and adjust the DMARC policy accordingly.
- Implement a tiered DMARC policy to balance the need for email deliverability with the risk of spam and phishing attacks.
- Leverage advanced analytics and reporting tools to optimise email deliverability and reduce the risk of false positives or spam and phishing attacks.
- Consider implementing email list segmentation, content optimisation, and complaint feedback loops to mitigate the risk of reputation damage during peak sending periods.
By following these best practices and carefully considering the impact of seasonal volume on DMARC policy, domain owners can help to ensure optimal email deliverability and reputation, even during peak sending periods. This requires a deep understanding of the domain's email ecosystem, as well as the ability to analyse and respond to DMARC aggregate reports in real-time. With the right approach and tools, domain owners can navigate the complexities of DMARC policy management and achieve optimal email deliverability and reputation.
Assessing Current DMARC Policy for Seasonal Adjustments
When managing DMARC policy for domains with seasonal sending patterns, it is crucial to assess the current policy setup to determine the best course of action for adjustments. This involves reviewing the existing DMARC record, SPF, and DKIM configurations to understand how they will impact mail flow during peak sending periods. A key consideration is the current DMARC policy's alignment with the organisation's email sending practices, including the use of third-party senders and mailing lists.
To begin, review the DMARC record for the domain, looking for the policy setting, such as p=none, p=quarantine, or p=reject. For example:
_dmarc.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1"In this example, the policy is set to p=none, which means that the domain is currently in monitoring mode, and no actions are being taken on messages that fail DMARC checks. The pct=100 setting indicates that 100% of messages are subject to DMARC checks.
For domains with seasonal sending patterns, it may be necessary to adjust the pct setting to a lower value during peak sending periods to prevent accidental blocking of legitimate email. However, this should be done with caution, as it can also increase the risk of spam and phishing attacks. A hosted or managed DMARC setup, such as the one provided by DMARC Engine, can help simplify this process by providing automated tools for adjusting DMARC policy settings and monitoring email traffic.
Another important aspect to consider is the SPF configuration, as it can significantly impact mail flow during peak sending periods. Review the SPF record to ensure that it includes all authorised senders, including third-party services and mailing lists. For example:
example.com. IN TXT "v=spf1 include:_spf.example.net include:mailinglist.example.com -all"In this example, the SPF record includes two authorised senders: _spf.example.net and mailinglist.example.com. It is essential to ensure that all authorised senders are included in the SPF record to prevent accidental blocking of legitimate email.
DKIM configuration is also critical, as it provides an additional layer of authentication for email messages. Review the DKIM record to ensure that it is correctly configured and that the selector and private key are valid. For example:
selector._domainkey.example.com. IN TXT "v=DKIM1; k=rsa; p=MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQCqGKukO1CrA48bQm4kzQlAl+4csOxUpckFWQsAvLscKP+5hVI/YCTx3AjBDZJo4V3G5JD/eJB4WHw6bqNevJ+wUyV6Hy8pNzloF52IBj4fhUAE1MlPC8d1DZehD2fvN6JHLJsL5M34/sAi9YZeeCi9Nq9SkUoyB2nPOkP+7P9vTtwIDAQAB"In this example, the DKIM record includes the public key and selector for the domain. It is essential to ensure that the DKIM record is correctly configured and that the private key is securely stored to prevent unauthorised access.
When assessing the current DMARC policy for seasonal adjustments, it is also essential to consider the organisation's email sending practices, including the use of third-party senders and mailing lists. Review the email traffic patterns to identify potential issues that may arise during peak sending periods. This includes monitoring email volume, sender IP addresses, and message content to ensure that they align with the organisation's email sending practices.
By carefully assessing the current DMARC policy and configurations, organisations can identify potential issues and make informed decisions about adjustments to ensure smooth mail flow during peak sending periods. This may involve adjusting the DMARC policy setting, SPF configuration, or DKIM setup to optimise email deliverability and prevent accidental blocking of legitimate email. A hosted or managed DMARC setup can provide additional tools and expertise to help organisations navigate these complex configurations and ensure optimal email deliverability.
Step-by-Step Guide to Adjusting DMARC Policy for Seasonal Sending
Adjusting DMARC policy for domains with seasonal sending patterns requires a careful, step-by-step approach to avoid disrupting email deliverability. The goal is to optimise the policy to centre around the domain's specific needs during peak and off-peak sending periods.
First, it is essential to assess the current DMARC policy and identify the areas that require adjustments. This involves analysing the domain's aggregate reports to determine the volume of emails sent, the authentication results, and the percentage of emails that fail DMARC. For instance, a domain may have a DMARC policy set to p=none during off-peak periods, which allows for a more relaxed authentication policy. However, during peak periods, the policy may need to be adjusted to p=quarantine or p=reject to protect against phishing attacks.
To adjust the DMARC policy, the following steps can be taken:
1. Determine the seasonal sending pattern: Analyse the domain's email sending volume over the past year to identify the peak and off-peak periods. This can be done by reviewing the aggregate reports or using a hosted DMARC solution that provides visualisations of the sending patterns. For example, a retailer may have a peak sending period during the holiday season, while a travel company may have a peak period during the summer months.
2. Assess the current DMARC policy: Review the current DMARC policy to determine if it is aligned with the domain's seasonal sending pattern. Check the policy's p value, which determines the action to take when an email fails DMARC authentication. A policy set to p=none will not take any action, while a policy set to p=quarantine or p=reject will take a more stringent approach.
3. Adjust the DMARC policy: Based on the analysis of the seasonal sending pattern and the current DMARC policy, adjust the policy to optimise it for the peak and off-peak periods. For example, during peak periods, the policy may be adjusted to p=quarantine to protect against phishing attacks, while during off-peak periods, the policy may be adjusted to p=none to allow for a more relaxed authentication policy.
Here is an example of a DMARC record snippet that demonstrates a policy adjustment for a peak period:
_dmarc.example.com. IN TXT "v=DMARC1; p=quarantine; pct=100; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1"In this example, the policy is set to p=quarantine, which means that emails that fail DMARC authentication will be quarantined. The pct value is set to 100, which means that the policy will be applied to all emails. The rua and ruf values specify the email addresses that will receive aggregate reports and forensic reports, respectively.
To adjust the DMARC policy, the p value can be changed to p=reject during peak periods to provide an additional layer of protection against phishing attacks. However, this should be done with caution, as it may cause legitimate emails to be rejected. Here is an example of a DMARC record snippet that demonstrates a policy adjustment for a peak period with a p=reject policy:
_dmarc.example.com. IN TXT "v=DMARC1; p=reject; pct=100; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1"In a hosted or managed setup, the DMARC policy can be adjusted using a web-based interface or API. For example, the DMARC Engine platform provides a web-based interface that allows users to adjust the DMARC policy and set custom rules for peak and off-peak periods.
When adjusting the DMARC policy, it is essential to monitor the aggregate reports to ensure that the policy is not causing any disruptions to email deliverability. The reports can provide valuable insights into the authentication results and help identify any issues that need to be addressed. For instance, if the reports show a high percentage of emails failing DMARC authentication, it may be necessary to adjust the policy to a more relaxed setting or to implement additional authentication mechanisms, such as SPF or DKIM.
In addition to adjusting the DMARC policy, it is also essential to optimise the SPF and DKIM records to ensure that they are aligned with the domain's seasonal sending pattern. This can be done by reviewing the SPF and DKIM records and adjusting them as necessary to ensure that they are correctly configured. For example, the SPF record may need to be adjusted to include additional IP addresses or domains that are used during peak periods.
Here is an example of an SPF record snippet that demonstrates an adjustment for a peak period:
example.com. IN TXT "v=spf1 ip4:192.0.2.1 ip4:198.51.100.1 include:_spf.example.net -all"In this example, the SPF record includes two IP addresses and a domain that are used during peak periods. The -all value at the end of the record specifies that all other IP addresses and domains should be rejected.
To optimise the DKIM record, it is essential to ensure that the record is correctly configured and that the selector is aligned with the domain's seasonal sending pattern. Here is an example of a DKIM record snippet that demonstrates an adjustment for a peak period:
selector._domainkey.example.com. IN TXT "v=DKIM1; k=rsa; p=MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQCqGKukO1De7zhZj6+H0qtjTkVxwTCpvKe4eCZ0FPqri0cb2JZfXJ/DgYSF6vUpwmJG8wVQZKjeGcjDOL5UlsuusFncCzWBQ7RKNUSesmQRMSGkVb1/3j+skZ6UtW+5u09lHNsj6tQ51s1SPrCBkedbNf0Tp0GbMJDyR4e9T04ZZwIDAQAB"In this example, the DKIM record includes a selector that is aligned with the domain's seasonal sending pattern. The p value specifies the public key that is used for DKIM signing.
In short, adjusting the DMARC policy for domains with seasonal sending patterns requires a careful, step-by-step approach that involves assessing the current policy, determining the seasonal sending pattern, and adjusting the policy to optimise it for peak and off-peak periods. By following these steps and optimising the SPF and DKIM records, domains can ensure that their email deliverability is not disrupted during peak periods and that they are protected against phishing attacks.
Aggregate Report Analysis for Data-Driven DMARC Decisions
When managing DMARC policy for domains with seasonal sending patterns, aggregate report analysis is crucial for making informed decisions. The reports, typically sent to the address specified in the DMARC record, contain valuable information about email authentication results, which can be used to adjust DMARC policy and improve deliverability. In a hosted or managed setup, such as the one we operate at DMARC Engine, these reports are collected and analysed daily to provide insights into authentication issues and potential threats.
To illustrate the importance of aggregate report analysis, consider a domain that experiences a significant increase in email volume during holiday seasons. Without analysing the aggregate reports, it may be challenging to identify potential authentication issues that could arise due to the increased volume. For instance, a sudden spike in email volume may lead to a higher percentage of emails failing DMARC authentication, which could result in a higher rate of emails being rejected by recipient mail servers. By analysing the aggregate reports, the domain owner can identify the root cause of the authentication issues and adjust the DMARC policy accordingly to prevent deliverability problems.
One of the key challenges in aggregate report analysis is dealing with the sheer volume of data. The reports can be extensive, containing information about every email that was sent from the domain, including authentication results, sender IP addresses, and recipient domains. To make sense of this data, it is essential to have a robust analysis system in place. At DMARC Engine, we use a combination of automated tools and manual analysis to process the aggregate reports and identify potential issues.
For example, we use automated scripts to parse the reports and extract relevant information, such as the number of emails that passed or failed DMARC authentication, the top sender IP addresses, and the most common recipient domains. This information is then used to generate a summary report that highlights potential issues and provides recommendations for improving DMARC policy.
Example of an aggregate report snippet:{
"org_name": "example.com",
"email": "dmarc@example.com",
"extra_contacts": [],
"report_id": "1234567890",
"report_metadata": {
"org_name": "example.com",
"email": "dmarc@example.com",
"extra_contacts": [],
"report_id": "1234567890"
},
"policy_published": {
"domain": "example.com",
"adkim": "r",
"aspf": "r",
"p": "none",
"sp": "none",
"pct": 100
},
"summary": {
"total": {
"messages": 1000,
"failed": 50,
"percentage": 5
}
},
"records": [
{
"row": {
"source_ip": "192.0.2.1",
"count": 500,
"policy_evaluated": {
"disposition": "none",
"dkim": "pass",
"spf": "pass"
}
}
},
{
"row": {
"source_ip": "198.51.100.1",
"count": 300,
"policy_evaluated": {
"disposition": "none",
"dkim": "fail",
"spf": "fail"
}
}
}
]
}In this example, the aggregate report snippet shows that the domain example.com has a DMARC policy published with an alignment mode of relaxed (adkim=r, aspf=r) and a policy of none (p=none). The report also shows that out of 1000 messages, 50 failed DMARC authentication, resulting in a failure percentage of 5. The records section of the report provides more detailed information about the emails that were sent, including the source IP address, count, and policy evaluation results.
By analysing this data, we can identify potential issues with the DMARC policy and provide recommendations for improvement. For instance, we may recommend adjusting the alignment mode to strict (adkim=s, aspf=s) to improve the security of the domain, or adjusting the policy to quarantine (p=quarantine) to prevent emails that fail DMARC authentication from being delivered to the recipient's inbox.
In addition to automated analysis, it is also essential to perform manual analysis of the aggregate reports to identify potential issues that may not be immediately apparent. For example, we may notice that a particular sender IP address is consistently failing DMARC authentication, which could indicate a problem with the sender's authentication configuration. By investigating this issue further, we can provide recommendations for improving the sender's authentication configuration and preventing future deliverability problems.
Another important aspect of aggregate report analysis is monitoring for potential threats, such as phishing attacks or spoofing attempts. By analysing the aggregate reports, we can identify potential threats and provide recommendations for mitigating them. For instance, we may recommend adjusting the DMARC policy to reject (p=reject) emails that fail DMARC authentication, or implementing additional security measures, such as BIMI (Brand Indicators for Message Identification), to prevent phishing attacks.
In a hosted or managed setup, such as the one we operate at DMARC Engine, aggregate report analysis is typically performed on a daily basis to ensure that any potential issues are identified and addressed promptly. This involves collecting and processing the aggregate reports, analysing the data, and providing recommendations for improving DMARC policy and preventing deliverability problems. By leveraging the expertise of a hosted or managed setup, domain owners can ensure that their DMARC policy is optimised for their specific needs and that they are protected against potential threats.
In conclusion to this section, aggregate report analysis is a critical component of DMARC policy management for domains with seasonal sending patterns. By analysing the aggregate reports, domain owners can identify potential issues with their DMARC policy, improve deliverability, and prevent potential threats. Whether using a hosted or managed setup, or performing analysis in-house, it is essential to have a robust analysis system in place to process the aggregate reports and provide insights into authentication results and potential threats.
Case Study: Implementing DMARC Policy Adjustments for Holiday Seasons
When managing DMARC policy for domains with seasonal sending patterns, it is crucial to centre your strategy around the unique characteristics of your organisation's email ecosystem. A key aspect of this involves adjusting DMARC policies to accommodate increased email volumes during holiday seasons, such as Christmas or Black Friday sales. In our experience at DMARC Engine, we have worked with numerous clients who require tailored DMARC policy adjustments to optimise their email deliverability during these critical periods.
For instance, consider a retail company that typically sends a moderate volume of emails throughout the year but experiences a significant surge in email traffic during the holiday season. Their DMARC policy is set to p=quarantine with a relatively low threshold for failed messages, which works well for their standard email volume. However, as the holiday season approaches, they anticipate a substantial increase in email sending, including promotional campaigns, transactional emails, and automated notifications.
To mitigate potential deliverability issues, our team works closely with the client to assess their current DMARC policy and identify areas for adjustment. We analyse their aggregate reports to determine the colour of their DMARC alignment, which indicates whether the messages are authentic or potentially spoofed.
// Example of an aggregate report snippet
{
"report_metadata": {
"org_name": "example.com",
"email": "dmarc@example.com",
"extra_contact_info": "https://example.com/dmarc",
"report_id": "1234567890",
"date_range": {
"begin": "2022-11-25T00:00:00Z",
"end": "2022-12-01T00:00:00Z"
}
},
"policy_published": {
"domain": "example.com",
"adkim": "r",
"aspf": "r",
"p": "quarantine",
"sp": "none",
"pct": "100"
},
"record": [
{
"row": {
"source_ip": "192.0.2.1",
"count": "100",
"policy_evaluated": {
"disposition": "quarantine",
"dkim": "pass",
"spf": "pass"
}
}
}
]
}Based on the analysis, we recommend adjusting the DMARC policy to p=none with a higher threshold for failed messages, allowing for more flexibility in email sending during the holiday season. This adjustment enables the client to maintain a good balance between email deliverability and spam prevention.
Another critical aspect to consider is the impact of seasonal sending patterns on SPF (Sender Policy Framework) and DKIM (DomainKeys Identified Mail) records. As email volumes increase, the likelihood of SPF and DKIM authentication failures also rises. To mitigate this risk, we advise clients to review their SPF and DKIM records to ensure they are correctly configured and aligned with their DMARC policy.
For example, a client may have an SPF record that includes a list of authorised senders, but during the holiday season, they may need to add temporary senders to support increased email traffic. In this case, we recommend using a managed SPF record service that allows for easy updates and optimisation of SPF records, ensuring that all authorised senders are included and reducing the risk of authentication failures.
// Example of an SPF record
"v=spf1 include:_spf.example.com ip4:192.0.2.1/24 include:mailgun.org -all"In addition to SPF and DKIM records, it is essential to consider the role of MTA-STS (Mail Transfer Agent Strict Transport Security) in maintaining email security during the holiday season. MTA-STS is a protocol that enables mail servers to declare their ability to support TLS encryption, which helps prevent eavesdropping and tampering with email communications.
To optimise MTA-STS for seasonal sending patterns, we recommend configuring MTA-STS records to include a list of authorised mail servers and setting a suitable policy for TLS encryption. This ensures that email communications are secure and reduces the risk of deliverability issues due to TLS-related problems.
// Example of an MTA-STS record
"version: STSv1
mode: enforce
mx: mail.example.com
max_age: 604800"In conclusion to this case study, implementing DMARC policy adjustments for holiday seasons requires careful planning, analysis, and optimisation of email authentication records. By working closely with clients to assess their unique email ecosystem and adjusting DMARC policies, SPF records, DKIM records, and MTA-STS configurations, we can help ensure optimal email deliverability and security during critical periods. Our experience at DMARC Engine has shown that a managed and hosted approach to DMARC policy management can provide significant benefits, including improved email deliverability, reduced risk of spam and phishing attacks, and enhanced overall email security.
Common Pitfalls in DMARC Policy Management for Seasonal Senders
Managing DMARC policy for domains with seasonal sending patterns can be complex, and several common pitfalls can lead to deliverability issues if not addressed properly. One of the primary concerns is setting the DMARC policy too strict, which can lead to legitimate emails being blocked during peak sending periods. For instance, if a domain has a policy set to p=reject, it may cause issues when the volume of emails increases suddenly, as the receiver's mail server may flag the sudden spike as suspicious activity.
A real-world example of this is a retail company that sends out a high volume of promotional emails during the holiday season. If their DMARC policy is set to p=reject, they may find that a significant portion of their emails are being blocked by receivers, resulting in lost sales and revenue. To mitigate this, it is essential to monitor the domain's DMARC aggregate reports closely and adjust the policy accordingly.
Example of a DMARC record with a strict policy:
_dmarc.example.com. IN TXT "v=DMARC1; p=reject; pct=100; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1"In a hosted or managed setup, such as the one provided by DMARC Engine, the team can work closely with the customer to adjust the DMARC policy and monitor the aggregate reports to ensure that the policy is optimised for their specific sending patterns.
Another common pitfall is not taking into account the impact of subdomains on the DMARC policy. If a domain has multiple subdomains, each with its own sending patterns, it is crucial to ensure that the DMARC policy is set up correctly for each subdomain. For example, if a company has a subdomain for marketing emails and another for transactional emails, the DMARC policy for each subdomain should be set up separately to reflect the different sending patterns.
Example of a DMARC record for a subdomain:
_dmarc.marketing.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1"Failing to do so can lead to deliverability issues, as the receiver's mail server may apply the DMARC policy of the parent domain to the subdomain, resulting in blocked or quarantined emails.
Also, not monitoring the DMARC aggregate reports regularly can lead to missed opportunities to optimise the DMARC policy. The aggregate reports provide valuable insights into the domain's sending patterns, including the number of emails sent, the number of emails that passed or failed DMARC, and the sources of the emails. By analysing these reports, domain owners can identify potential issues and adjust the DMARC policy accordingly. For instance, if the reports show a high number of emails failing DMARC due to SPF alignment issues, the domain owner can adjust the SPF record to include the missing sources.
It is also essential to consider the impact of third-party senders on the DMARC policy. If a domain uses third-party senders, such as marketing automation platforms or email service providers, it is crucial to ensure that these senders are included in the DMARC policy. Failing to do so can lead to deliverability issues, as the receiver's mail server may flag the emails sent by the third-party sender as suspicious activity.
Example of a DMARC record that includes a third-party sender:
_dmarc.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1; spf=a include:_spf.example.net -all"In a hosted or managed setup, the team can work with the customer to identify the third-party senders and ensure that they are included in the DMARC policy.
Also, domain owners should be aware of the potential impact of DMARC on their email deliverability during peak sending periods. If the domain's DMARC policy is set too strict, it may cause issues with email deliverability, resulting in lost revenue and reputation damage. To mitigate this, domain owners should consider adjusting their DMARC policy during peak sending periods to ensure that legitimate emails are not blocked. For example, they may consider setting the DMARC policy to p=none during peak sending periods, and then switching back to a stricter policy during off-peak periods.
In conclusion to this section, managing DMARC policy for domains with seasonal sending patterns requires careful consideration of several factors, including the impact of subdomains, third-party senders, and peak sending periods. By monitoring the DMARC aggregate reports regularly and adjusting the DMARC policy accordingly, domain owners can ensure that their emails are delivered to the inbox and that their reputation is protected. A hosted or managed setup can provide additional support and expertise to help domain owners navigate the complexities of DMARC policy management.
To centre the DMARC policy management strategy around the domain's specific needs, it is essential to conduct regular analysis of the aggregate reports and adjust the policy accordingly. This may involve adjusting the policy to p=none during peak sending periods, and then switching back to a stricter policy during off-peak periods. By taking a proactive and data-driven approach to DMARC policy management, domain owners can optimise their email deliverability and protect their reputation.
The colour of the DMARC policy, in terms of its strictness, should be carefully considered to ensure that it is optimised for the domain's specific sending patterns. A policy that is too strict may cause issues with email deliverability, while a policy that is too lenient may leave the domain vulnerable to spam and phishing attacks. By finding the right balance, domain owners can ensure that their emails are delivered to the inbox and that their reputation is protected.
To optimise the DMARC policy, domain owners should consider implementing a system to regularly review and update the policy. This may involve setting up a calendar reminder to review the policy on a regular basis, or implementing a system to automatically adjust the policy based on the domain's sending patterns. By taking a proactive and data-driven approach to DMARC policy management, domain owners can ensure that their emails are delivered to the inbox and that their reputation is protected.
In terms of organising the DMARC policy management strategy, it is essential to consider the domain's specific needs and sending patterns. This may involve setting up a system to track the
Optimising DMARC Policy for Multiple Domains and Subdomains
When managing DMARC policy for domains with seasonal sending patterns, one of the most complex challenges is optimising policy for multiple domains and subdomains. This is particularly true for large organisations with diverse email ecosystems, where a single policy change can have far-reaching consequences. At DMARC Engine, we have seen firsthand the importance of carefully considering the interplay between domains, subdomains, and DMARC policy.
A key consideration is the use of organisational domains versus subdomains for email sending. For example, a company might use example.com for corporate emails, news.example.com for newsletters, and alerts.example.com for automated alerts. Each of these domains and subdomains may have its own DMARC policy, which can lead to complexity in management and analysis.
# Example DMARC record for example.com
_dmarc.example.com. IN TXT "v=DMARC1; p=quarantine; pct=100; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1"In a hosted or managed setup like DMARC Engine, we can simplify this process by providing a centralised dashboard for managing DMARC policy across multiple domains and subdomains. This allows administrators to easily apply consistent policies, monitor aggregate reports, and make data-driven decisions.
However, even with a managed setup, there are trade-offs to consider when optimising DMARC policy for multiple domains and subdomains. One such trade-off is the balance between policy restrictiveness and the risk of false positives. A more restrictive policy (e.g., p=reject) can provide stronger protection against spoofing, but may also increase the likelihood of legitimate emails being blocked. This is particularly concerning for domains or subdomains with high volumes of automated or transactional emails, where a single misplaced policy change can have significant business impacts.
To mitigate this risk, we recommend a gradual approach to policy implementation, starting with a more permissive policy (e.g., p=none) and gradually increasing restrictiveness as the organisation becomes more comfortable with DMARC and its implications. This approach also allows for the identification and remediation of any potential issues or misconfigurations before they become critical.
Another important consideration is the use of subdomain policies versus organisational domain policies. In general, it is recommended to apply DMARC policy at the organisational domain level, rather than the subdomain level. This provides a more comprehensive view of email sending patterns and allows for more effective monitoring and analysis. However, there may be cases where a subdomain-specific policy is necessary, such as when a subdomain is used for a specific business unit or application with unique email sending requirements.
# Example DMARC record for news.example.com
_dmarc.news.example.com. IN TXT "v=DMARC1; p=quarantine; pct=100; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1"In such cases, it is essential to carefully consider the potential interactions between subdomain policies and organisational domain policies, to avoid unintended consequences or conflicts.
Finally, it is crucial to remember that DMARC policy optimisation is an ongoing process, rather than a one-time task. As email sending patterns and business requirements evolve, DMARC policy must also adapt to ensure continued effectiveness and protection against spoofing. At DMARC Engine, we recommend regular review and analysis of aggregate reports, as well as ongoing monitoring of email sending patterns and DMARC policy performance. By taking a proactive and data-driven approach to DMARC policy management, organisations can optimise their policy for multiple domains and subdomains, and maintain a strong defence against email-based threats.
Monitoring and Maintaining DMARC Policy Over Time
To ensure the long-term effectiveness of DMARC policy for domains with seasonal sending patterns, it is crucial to establish a robust monitoring and maintenance routine. This involves regularly analysing aggregate reports, adjusting policy settings as needed, and keeping a close eye on authentication results. At DMARC Engine, we have seen firsthand the importance of proactive DMARC management, particularly for domains that experience significant fluctuations in email volume.
One key aspect of monitoring DMARC policy is tracking changes in authentication results over time. For instance, a domain may observe a spike in SPF failures during peak sending seasons, which could indicate issues with mail server configuration or IP address rotation. By analysing aggregate reports, domain owners can identify such trends and take corrective action to optimise their SPF records.
Example of an aggregate report snippet:
{
"org_name": "example.com",
"date_range": {
"start": "2022-12-01",
"end": "2022-12-31"
},
"records": [
{
"row": {
"source_ip": "192.0.2.1",
"count": 1000,
"disposition": "none",
"dkim": "pass",
"spf": "fail"
}
}
]
}In this example, the domain owner would need to investigate the SPF failure and adjust their SPF record accordingly, possibly by adding or removing IP addresses.
Another critical aspect of DMARC policy maintenance is adjusting the policy setting itself. For domains with seasonal sending patterns, it may be necessary to relax the DMARC policy during peak seasons to prevent legitimate emails from being blocked. However, this must be done carefully, as relaxing the policy too much can leave the domain vulnerable to spoofing attacks. A common approach is to switch from a quarantine or reject policy to a none policy during peak seasons, while closely monitoring aggregate reports for any signs of abuse.
In a hosted or managed setup, such as DMARC Engine, the process of monitoring and maintaining DMARC policy is often automated and streamlined. For example, our platform provides real-time analytics and alerts for changes in authentication results, allowing domain owners to respond quickly to potential issues. Also, our system can automatically adjust DMARC policy settings based on predefined rules and thresholds, ensuring that the domain remains protected without disrupting legitimate email traffic.
When maintaining DMARC policy over time, it is also essential to keep in mind the potential impact of external factors, such as changes in mail server configuration or updates to authentication protocols. For instance, the adoption of BIMI (Brand Indicators for Message Identification) may require adjustments to DMARC policy, as BIMI relies on DMARC authentication to verify the identity of the sender. By staying up-to-date with the latest developments in email authentication and security, domain owners can ensure that their DMARC policy remains effective and aligned with industry best practices.
In terms of specific recommendations, we suggest that domain owners with seasonal sending patterns review their DMARC policy and aggregate reports at least quarterly, or more frequently during peak seasons. This review should include an analysis of authentication results, policy setting adjustments, and verification of mail server configuration and IP address rotation. By following this routine and staying proactive, domain owners can optimise their DMARC policy for seasonal sending patterns and maintain a high level of email deliverability and security.
To illustrate the importance of regular review and adjustment, consider the example of a retail company that sends a high volume of promotional emails during the holiday season. If the company fails to adjust its DMARC policy accordingly, it may experience a significant increase in blocked or quarantined emails, resulting in lost revenue and damaged reputation. By monitoring aggregate reports and adjusting the DMARC policy in advance, the company can ensure that its emails are delivered successfully and that its brand is protected from spoofing attacks.
In conclusion to this section, monitoring and maintaining DMARC policy over time is a critical aspect of email security and deliverability for domains with seasonal sending patterns. By establishing a robust routine of analysis and adjustment, domain owners can optimise their DMARC policy, prevent email disruptions, and protect their brand from spoofing attacks. At DMARC Engine, we are committed to helping our customers navigate the complexities of DMARC policy management and ensuring the long-term effectiveness of their email security measures.